Zoubir Mammeri

A schedulability analysis of tasks and network traffic in distributed real-time systems

In real-time systems the deadlines of each task must be met. A pre-run-time schedulability analysis becomes necessary to prove that the existing software and target hardware will meet the real-time application constraints. In a real-time distributed system, the messages transmitted through the network are also time constrained. However, some new problems arise when one applies existing task scheduling algorithms to schedule the network traffic. The main goal of this paper is to define the boundaries between these two domains of scheduling. After an introduction to fieldbuses and real-time systems, we present an equivalence between tasks and messages as well as between processors and networks, which are much different in practice but have strong similarities from the scheduling point of view. Finally, we analyse the new constraints introduced by the presence of smart transducers/transmitters in fieldbus applications and we define the scheduling algorithms adapted for this type of applications.

Neural networks for multiprocessor real-time scheduling

In recent years, neural networks have become a popular area of research, especially after Hopfield and Tank opened the way for using neural networks for optimization purposes and surprised the scientific community by their paper (Biological Cybernetics, vol. 52, pp. 141-52, 1985) presenting a circuit to give approximate solutions for the classical traveling salesman problem in a few elapsed propagation times of analog amplifiers. In this paper, we analyze Hopfield neural networks from the scheduling viewpoint to see if they can be used to solve real-time scheduling problems. We build a neural network whose topology depends on real-time task constraints, and converges to an approximate solution of the scheduling problem. Finally, we analyze the quality of the result in terms of the convergence rate and the complexity of the algorithm.<<ETX>>

Handling precedence constraints with neural network based real-time scheduling algorithms

In previous work, the authors proposed an approach to the approximate solution of scheduling problems, neural network based algorithms, applied to the preemptive and non-preemptive scheduling for a mono or multiprocessor environment. Results were presented in a systematic approach for translating task constraints into neural network building rules that are independently added to the neural architecture. The main advantage of this methodology is that the neural network built according the rules converges to a solution of the scheduling problem in only a few propagation times of analogue amplifiers. They present new rules that extend the methodology to handle precedence constraints. They present the formal energy function which occurs when the precedence constraints are met and finally present a performance analysis of the quality of the results obtained by this approach.

Solving real-time scheduling problems with Hopfield-type neural networks

Real-time applications are increasingly becoming more complex, leading to the necessary development of fast scheduling algorithms. Therefore, the use of algorithms with a parallel search of feasible schedules seems to be attractive. In turn, Hopfield-type neural networks are suitable to solve complex combinatorial problems, owing to their fast convergence, if analog hardware is implemented. However, these neural networks have associated concepts of sub-optimality and the possibility of unfeasible solutions, which are contrary to the notion of system predictability. The paper presents a systematic procedure to map the scheduling problem onto a neural network in such a way that network solutions are always feasible schedules. Network convergence time is studied with digital computer simulations, using a discrete time model. Global asymptotic consistency between the discrete time model and the continuous one is assured. The paper also presents an analysis of the complexity of the proposed method.

Real-Time Software Architecture: Application to FIP Fieldbus

Abstract In time-critical communication, temporal mechanisms and scheduling algorithms are required to meet time constraints at run-time of distributed applications. This paper deals with mechanisms to integrate at the various levels of a communication architecture to qualify with a temporal view point the data exchanged between distributed application processes. The proposed mechanisms are applied to FIP fieldbus showing how to elaborate temporal statuses of FIP by invoking a general state machine.

Compu-search methodologies II: Scheduling using genetic algorithms and artificial neural networks

This chapter complements the previous chapter ‘Scheduling Methodology: Optimization and Compu-search Approaches I’ about the scheduling level of a production manufacturing hierarchical approach. It presents various ways of using genetic algorithms and artificial neural networks to solve scheduling problems. Genetic algorithms are used for scheduling problems without assignment unknown values (solutions are completely decribed by the list of job sequences on each resource). The potential use of artificial neural networks for solving scheduling problems is illustrated with a simple multiprocessor scheduling problem.

Neural network versus max-flow algorithms for multiprocessor real-time scheduling

Neural networks have been widely used in a large area of applications, like image processing, learning processes, identification and control, etc. but there is a lack for their use for approximate solving real-time scheduling problems. The authors have already shown the ability of a neural network based scheduling algorithm to deal with the scheduling of independent real-time tasks in a multiprocessor environment. The algorithm is approximate but has a remarkable convergence speed due to the highly parallel nature of the search. In recent literature, the authors have analyzed the performance of the algorithm when compared with the well-known rare monotonic and earliest deadline algorithms for the monoprocessor case. In this paper we present an analysis of the quality of the yielded solution for the multiprocessor case.

MMS and time

This paper addresses MMS and real time issues. The requirements for real time systems are introduced. The paper also presents briefly MAP, and in particular, the MMS standard in real time environments, emphasising its weaknesses and goes on to show our approach to making MMS more suitable for a real time context. The approach consists of modifying the existing objects, services and protocol to satisfy real time requirements.

On tasks synchronization with the MMS protocol

The MMS protocol is a powerful tool for communication as well as for synchronization between manufacturing equipment such as robots, programmable logic controllers, etc. This paper shows how the MMS services can be used to handle tasks synchronization in a distributed environment. The MMS weaknesses regarding temporal aspects and possible directions to alleviate these weaknesses are also presented.

A state-machine for temporal qualification of time-critical communication

The paper deals with the definition of time constraints on data validity and proposes mechanisms which may be included in protocols to verify on line if constraints are met or not. The authors introduce a state-machine to integrate mechanisms in the communication protocols to inform application processes about the temporal validity of the information they consume.<<ETX>>